We show that ESF maps can reveal a rich landscape of undiscovered structures and properties for known molecules 22, as well as predicting the properties of hypothetical molecules for specific target applications, before realizing these in the laboratory.Ĭandidate building blocks for porous solids. Here, we use ESF maps to guide us to molecular materials with remarkable porosity levels and high predicted gas selectivities, avoiding any assumptions or intuitive guesses about the crystal packing. Without such built-in porosity, the lowest energy crystal structures for molecules are, with few exceptions 21, close-packed and non-porous. Previously, we predicted the crystal structures of organic cages 19, 20, where most of the possible crystal packings are porous because of the intrinsically porous molecular structure. We illustrate this function mapping approach for porous organic molecular crystals 17, which are rare because molecules tend to pack densely 18. Each structure’s likelihood of being stable and accessible to experiment relates to its predicted lattice energy. Each predicted structure encodes a set of physical properties: this ensemble of structures and their properties defines an energy–structure–function (ESF) map, representing the possible material properties associated with the molecule. Crystal structure prediction (CSP) methods 15, 16 have been developed to determine the stable crystalline arrangements that are available to a molecule. The a priori design of functional molecular crystals, therefore, demands a predictive strategy that does not rely on intuitive bonding rules or assumed topologies. Hence, there are few molecular analogues of isoreticular MOFs 13 or covalent organic frameworks 14, whose lattice energies are dominated by a specific bonding pattern across a broad range of building blocks. Likewise, polymorphism is commonplace in molecular crystals 12. It is therefore difficult to apply structure–function relationships learned from one system to a new molecule, which might pack in a totally different way. Hence, small changes to molecular structure can cause profound changes in crystal packing. This design challenge is particularly acute for molecular crystals, whose complex structural landscapes are defined by competing, weak structure-determining interactions. To predict, select, and then synthesize new functional materials, we need a simple, digestible description of the probable structure–function space, rather than the potential space, which will always be astronomically large 11. However, these methods are based on assumed framework topologies and they do not tell us about the relative energies of the hypothetical structures and which structures, if any, can be synthesized. Inexpensive calculations have been used to enumerate large libraries of metal–organic frameworks (MOFs) and to predict their gas adsorption properties 7, 10. Computational prediction of both stability and function has great potential to discover materials with arresting properties 6, 8, 9 but it is difficult in practice because of the computational expense of exploring vast structural landscapes, coupled with the need for accurate lattice energies and reliable property predictions. This requires us to compute both the property of interest and the material’s stability with respect to alternative atomic configurations. It is a major challenge for computational materials research, however, to identify new materials that are more than hypothetical. Predictive calculations of material structure and properties have been used successfully for zeolites 1, new allotropes of common elements 2, cathode materials for batteries 3, redox-active frameworks 4, organic photovoltaics 5, metal oxides 6 and porous solids 7. More generally, energy–structure–function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties. Both crystal structure and physical properties, such as the methane storage capacity and guest selectivity, are predicted using the molecular diagram as the only input. Using these maps, we identify a highly porous solid with the lowest density reported for a molecular crystal. Here, we combine computational crystal structure prediction and property prediction to build energy–structure–function maps describing the possible structures and properties available to a candidate molecule. Hence, design strategies that assume a topology or other structural blueprint will often fail. Their structure results from the balance of many weak interactions, unlike the strong and predictable bonding patterns found in metal–organic frameworks and covalent organic frameworks. Molecular crystals cannot be designed like macroscopic objects because they do not assemble according to simple, intuitive rules.